我国尺度兼容和树种分类的材积源森林碳储量模型

doi:10.11707/j.1001-7488.LYKX20230562

Abstract

Abstract:

Objective: A simple and convenient method for estimating forest carbon storage was proposed, and a scale-compatible and tree species-classified forest carbon storage model of volume-derived was constructed, which provided a method and technology support for estimating forest carbon storage of multi-scale and multi-tree species. Method: Based on the data of the 6th?9th national forest resource inventory and allometric growth equation, the scale-compatible and tree species-classified stand carbon storage model considering stand origin and age group was constructed by using the independent model of non-linear least square method and the simultaneous equations model of non-linear seemingly unrelated regressions with dummy variables. Heteroscedasticity was eliminated by weighted regression, and the model was evaluated by determination coefficient (R²), standard error of estimate (SEE), mean prediction error (MPE), total relative error (TRE) and variance percentage (VP). Meanwhile, using the data of the National forest and grass ecological comprehensive monitoring in 2021, the differences of forest carbon storage estimation by different scale models were compared. Result: 1) A total of 2 974 scale-compatible forest carbon storage models were constructed. Compared with the independent model, there was no significant difference in R² of the simultaneous equations model. The independent model and the simultaneous equations model were 1 383 and 1 591 categories, respectively. The average values of the model R² were 0.966 1 and 0.965 2, MPE were 0.75% and 0.78%, respectively, the R² of the simultaneous equations model only decreased by 0.000 9, and the MPE only increased by 0.03%. 2) A total of 2 520 tree species-classified forest carbon storage models were constructed. As with the results of the scale-compatible model, there was no significant difference in R² between the independent model and the simultaneous equations model. The independent model and the simultaneous equations model were both 1 260 categories. The average values of the model R² were 0.944 3 and 0.942 4, MPE were 0.48% and 0.49%, respectively, the R² of the simultaneous equations model only decreased by 0.001 9, and the MPE only increased by 0.01%. 3) Four forest carbon storage models with different modeling methods (independent-scale model, independent-tree species model, simultaneous-scale model, simultaneous-tree species model) were established. Compared with the independent model, the parameters variation of the simultaneous equations model was smaller. Four forest carbon storage models with different modeling methods contained 46 157 and 23 935 parameters a and b respectively. The average values of parameter a in the independent model and the simultaneous equations model were 0.596 5 and 0.620 0, respectively, and the ranges were 2.318 6 and 2.192 2, respectively. The range of parameters in the independent model was 0.126 4 higher. The average values of parameter b were 0.933 2 and 0.931 8, respectively, and the ranges were 0.672 3 and 0.505 6, respectively. The range of the independent model was 0.166 7 higher. 4) When estimating national forest carbon storage by different scale models, regardless of the scale, the estimation difference of the independent model was higher than the simultaneous model. However, in general, the estimation differences at various scales were within 3%. Conclusion: 1) The volume-derived forest carbon storage model proposed in this paper from stand volume to stand carbon storage was effective and reliable, which can be used to directly estimate forest carbon storage. 2) Based on the simultaneous equations model of non-linear seemingly unrelated regressions with dummy variables, the scale-compatible and tree species-classified forest carbon storage model can be better established. 3) The average R² of the forest carbon storage model constructed in this paper was above 0.95, and MPE was less than 1%, which can be used to quickly and accurately estimate forest carbon storage in forestry practice. 4) According to the fitting accuracy of the model and the stability of the parameters, we recommend using the simultaneous-tree species model. 5) Under the accuracy requirement of 5%, the national scale model can be used to estimate the national forest carbon storage.

Key words: forest carbon storage model, nonlinear likelihood independent, dumb variable, parameter database, scale-compatible, tree species-classified

CLC Number:

S757

Cong Zhang,Qi Liu,Haikui Li,Pengju Liu,Siying Zhan. Scale-Compatible and Tree Species-Classified Forest Carbon Storage Model of Volume-Derived in China[J]. Scientia Silvae Sinicae, 2025, 61(1): 57-69.

Figures/Tables 13

Table 1

Table 2

Table 3

Table 4

Climate regional scale division of China"

尺度Scale	具体尺度Specific scale
国家 Nation	全国 Nationwide
气温区 Temperature region	北温带、中温带、南温带、北亚热带、中亚热带、南亚热带、北热带、中热带、高原气候区 North temperate zone, middle temperate zone, south temperate zone, north subtropical zone, middle subtropical zone, south subtropical zone, north tropical zone, middle tropical zone, plateau climate zone
气候区 Climatic region	滇南河谷区、雷琼区、琼西区、元江区、根河区、江北区、秦巴区、波密_川西区、藏南区、柴达木区、昌都区、达旺_察隅区、祁连_青海湖区、青南区、河北区、晋陕甘区、辽东_胶东半岛区、鲁淮区、南疆区、渭河区、滇南区、闽南_珠江区、琼南_西沙区、北疆区、大兴安岭区、富蕴区、蒙东区、蒙甘区、蒙中区、三江_长白区、松辽区、塔城区、小兴安岭区、伊宁区、滇北区、贵州区、江南区、金沙江_楚雄_玉溪区、瓯江_闽江_南岭区、四川区 Yunnan river valley, Leiqiong district, Qiongxi district, Yuanjiang district, Genhe district, Jiangbei district, Qinba district, Bomi_western Sichuan district, southern Xizang district, Qaidam district, Changdu district, Dawang_Chayu district, Qilian_Qinghai Lake district, southern Qinghai district, Hebei district, Shanxi_Shaanxi_Gansu district, Liaodong_Jiaodong Peninsula district, Luhuai district, southern Xinjiang district, Weihe district, southern Yunnan district, southern Fujian_Pearl River district, southern Fujian_Xisha district, northern Xinjiang district, Daxing’anling district, Fuyun district, eastern Inner Mongolia district, Inner Mongolia_Gansu district, central Mongolia district, Sanjiang_Changbai district, Songliao district, Tacheng district, Xiaoxing’anling district, Yining district, northern Yunnan district, Guizhou district, Jiangnan district, Jinsha River_Chuxiong_Yuxi district, Oujiang River_Minjiang River_Nanling district, Sichuan district

Table 4

Table 5

Fig.1

Table 6

Model parameter a of the province at the whole forest level without considering stand characteristics variables (estimated value ± SD)"

省份 Province	样本量 Sample size	独立模型 Independent model	联立方程组模型 Simultaneous equations model	省份 Province	样本量 Sample size	独立模型 Independent model	联立方程组模型 Simultaneous equations model
北京 Beijing	2 803	0.753 2±0.004 4	0.794 3±0.004 3	湖北 Hubei	6 064	0.681 7±0.003 1	0.718 7±0.002 8
天津 Tianjin	501	0.651 2±0.008 5	0.686 4±0.009 2	湖南 Hunan	8 175	0.597 5±0.002 6	0.629 6±0.002 3
河北 Hebei	4 999	0.694 3±0.003 4	0.731 3±0.003 1	广东 Guangdong	5 124	0.674 3±0.003 2	0.709 8±0.003 0
山西 Shanxi	4 503	0.803 3±0.003 8	0.847 6±0.003 5	广西 Guangxi	5 193	0.647 1±0.003 0	0.683 2±0.002 8
内蒙古 Inner Mongolia	9 442	0.585 4±0.002 4	0.620 9±0.001 8	海南 Hainan	2 539	0.844 7±0.004 3	0.895 8±0.003 8
辽宁 Liaoning	3 785	0.733 6±0.003 6	0.776 4±0.003 2	重庆 Chongqing	4 489	0.612 0±0.003 0	0.647 4±0.002 7
吉林 Jilin	13 176	0.734 2±0.002 8	0.781 4±0.001 8	四川 Sichuan	7 567	0.597 0±0.002 6	0.637 0±0.001 9
黑龙江 Heilongjiang	13 293	0.655 0±0.002 5	0.694 1±0.001 7	贵州 Guizhou	5 117	0.606 5±0.002 9	0.641 0±0.002 7
上海 Shanghai	647	0.681 6±0.007 8	0.718 1±0.008 7	云南 Yunnan	12 548	0.674 5±0.002 7	0.716 5±0.001 8
江苏 Jiangsu	2 775	0.602 4±0.003 7	0.635 2±0.003 7	西藏 Xizang	2 991	0.570 8±0.002 8	0.614 1±0.002 2
浙江 Zhejiang	6 213	0.644 4±0.003 0	0.678 9±0.002 7	陕西 Shaanxi	6 221	0.852 1±0.003 6	0.899 4±0.003 0
安徽 Anhui	8 682	0.644 9±0.002 7	0.680 2±0.002 3	甘肃 Gansu	7 210	0.686 0±0.002 9	0.731 9±0.002 2
福建 Fujian	9 579	0.637 2±0.002 6	0.675 7±0.001 9	青海 Qinghai	3 401	0.676 7±0.003 3	0.719 8±0.002 9
江西 Jiangxi	4 409	0.640 3±0.003 3	0.674 8±0.003 1	宁夏 Ningxia	1 105	0.675 5±0.005 8	0.712 9±0.006 2
山东 Shandong	2 977	0.654 7±0.004 0	0.688 9±0.004 1	新疆 Xinjiang	4 062	0.545 0±0.002 5	0.583 1±0.002 0
河南 Henan	6 281	0.744 9±0.003 3	0.785 0±0.002 9

Table 6

Table 7

Table 8

Model parameter a of the forest subclass at the national scale without considering stand characteristics variables (estimated value± SD)"

森林亚类 Forest subclass	样本量 Sample size	独立模型 Independent model	联立方程组模型 Simultaneous equations model
冷杉 Abies spp.	2 525	0.449 6±0.001 4	0.488 2±0.001 5
云杉 Picea spp.	5 673	0.517 7±0.001 5	0.559 2±0.001 5
铁杉 Tsuga spp.	78	0.460 5±0.004 7	0.500 0±0.007 0
油杉 Keteleeria spp.	215	0.600 5±0.007 3	0.636 2±0.010 7
落叶松 Larix spp.	9 215	0.476 3±0.001 3	0.510 3±0.001 3
红松 Pinus koraiensis	333	0.573 1±0.003 9	0.614 9±0.005 6
樟子松 Pinus sylvestris var. mongolica	583	0.471 2±0.003 0	0.504 1±0.004 3
赤松 Pinus densiflora	282	0.753 2±0.008 3	0.797 4±0.012 1
黑松 Pinus thunbergii	354	0.771 0±0.007 5	0.814 1±0.010 9
油松 Pinus tabuliformis	4 445	0.654 9±0.002 0	0.695 6±0.002 6
华山松 Pinus armandii	1 192	0.567 2±0.002 8	0.604 6±0.003 9
马尾松 Pinus massoniana	14 350	0.503 9±0.001 2	0.535 8±0.001 4
云南松 Pinus yunnanensis	3 508	0.469 4±0.001 6	0.500 4±0.002 1
思茅松 Pinus kesiya var. langbianensis	483	0.614 2±0.003 8	0.655 6±0.005 5
高山松 Pinus densata	787	0.556 4±0.002 5	0.598 4±0.003 4
国外松 Foreign pine	1 139	0.613 1±0.007 6	0.653 6±0.011 1
黄山松 Pinus taiwanensis	99	0.593 6±0.011 2	0.630 3±0.016 5
其他松类 Other pine trees	249	0.824 7±0.008 4	0.874 4±0.012 3
杉木 Cunninghamia spp.	13 516	0.454 3±0.001 2	0.484 2±0.001 3
柳杉 Cryptomeria spp.	386	0.482 5±0.003 6	0.517 1±0.005 3
水杉 Metasequoia spp.	220	0.427 1±0.005 3	0.454 8±0.007 8
柏木 Cupressus spp.	4 970	0.678 3±0.002 1	0.719 6±0.002 7
栎类 Quercus spp.	18 379	0.786 2±0.001 8	0.840 6±0.001 7
桦木 Betula spp.	4 079	0.609 4±0.001 8	0.649 6±0.002 2
白桦 Betula platyphylla	4 866	0.554 2±0.001 6	0.591 2±0.001 9
枫桦 Betula Costata	218	0.635 1±0.005 8	0.677 4±0.008 4
水胡黄 Fraxinus mandshurica, Juglans mandshurica, Phellodendron	622	0.838 0±0.008 1	0.889 5±0.011 9
樟木 Cinnamomum spp.	383	0.732 4±0.006 8	0.774 1±0.009 9
楠木 Phoebe spp.	89	0.667 5±0.009 5	0.714 1±0.014 1
榆树 Ulmus spp.	1 100	0.669 0±0.004 2	0.706 5±0.006 0
刺槐 Robinia spp.	852	0.638 2±0.005 2	0.671 1±0.007 5
木荷 Schima spp.	356	0.709 9±0.005 7	0.756 1±0.008 3
枫香 Liquidambar spp.	244	0.670 8±0.007 3	0.711 6±0.010 6
其他硬阔类 Other hard-and-broad trees	8 192	0.807 2±0.002 1	0.860 1±0.002 2
椴树 Tilia spp.	684	0.621 5±0.003 1	0.665 4±0.004 4
杨树 Populus spp.	13 878	0.541 1±0.001 3	0.575 5±0.001 4
柳树 Salix spp.	419	0.651 3±0.006 1	0.689 1±0.008 9
泡桐 Paulownia spp.	333	0.706 0±0.007 1	0.745 2±0.010 3
桉树 Eucalyptus spp.	2 556	0.633 3±0.002 6	0.669 2±0.003 6
相思 Abrus spp.	221	0.573 8±0.006 3	0.610 0±0.009 3
木麻黄 Casuarina equisetifolia	134	0.506 1±0.008 3	0.537 1±0.012 3
楝树 Melia spp.	30	0.613 6±0.025 6	0.645 6±0.037 3
其他软阔类 Other soft-and-broad trees	5 174	0.657 7±0.002 0	0.699 7±0.002 4
针叶混交林 Mixed coniferous forest	4 871	0.514 0±0.001 5	0.549 8±0.001 8
阔叶混交林 Mixed broad-leaved forest	32 545	0.723 5±0.001 6	0.773 0±0.001 4
针阔混交林 Mixed broad-leaf and coniferous forest	10 978	0.596 8±0.001 5	0.637 3±0.001 6

Table 8

Table 9

Table 10

Table 11

Fig.2

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行政大区 Administrative region	森林蓄积量 Forest volume/(m³?hm^?2)	森林碳储量 Forest carbon storage/(t?hm^?2)
东北 Northeast	119.33±82.28	52.67±36.27
华北 North China	77.50±66.43	33.04±26.24
华东 East China	72.00±66.86	30.05±27.67
华南 South China	64.33±61.22	29.08±28.78
西北 Northwest	118.31±121.28	49.14±41.53
西南 Southwest	120.15±141.21	46.17±47.78

森林类 Forest class	森林蓄积量 Forest volume/ (m³?hm^?2)	森林碳储量 Forest carbon storage/ (t?hm^?2)
阔叶纯林Pure broad-leaved forest	79.63±75.71	38.21±36.06
阔叶混交林 Mixed broad-leaved forest	99.95±80.92	50.03±38.84
针阔混交林 Mixed broad-leaf and coniferous forest	93.44±85.59	38.66±32.90
针叶纯林 Pure coniferous forest	105.21±120.78	36.10±36.64
针叶混交林 Mixed coniferous forest	103.21±103.05	36.43±33.48

尺度Scale	具体尺度Specific scale
国家 Nation	全国 Nationwide
区域 Region	北方、南方Northern and southern
行政大区 Administrative region	东北、华北、西北、华南、华东、西南Northeast, north China, northwest, south China, east China, southwest
省份 Province	黑龙江、吉林、辽宁、北京、天津、河北、山西、内蒙古、陕西、宁夏、甘肃、青海、新疆、山东、江苏、上海、安徽、浙江、江西、福建、河南、湖北、湖南、广西、广东、海南、重庆、贵州、四川、云南、西藏Heilongjiang, Jilin, Liaoning, Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Shaanxi, Ningxia, Gansu, Qinghai, Xinjiang, Shandong, Jiangsu, Shanghai, Anhui, Zhejiang, Jiangxi, Fujian, Henan, Hubei, Hunan, Guangxi, Guangdong, Hainan, Chongqing, Guizhou, Sichuan, Yunnan, Xizang
副总体Subpopulation	甘肃、吉林、黑龙江、新疆和内蒙古划分为2~4个副总体 Gansu, Jilin, Heilongjiang, Xinjiang and Inner Mongolia were divided into 2?4 subpopulations

森林类型层级 Forest type level	具体树种（组）Specific tree species (group)
全部森林 Whole forest	全部树种 All tree species
森林类 Forest type	阔叶纯林、阔叶混交林、针阔混交林、针叶纯林、针叶混交林 Pure broad-leaved forest, mixed broad-leaved forest, mixed broad-leaf and coniferous forest, pure coniferous forest, mixed coniferous forest
森林亚类 Forest subclass	桉树、白桦、柏木、赤松、刺槐、椴树、枫桦、枫香、高山松、国外松、黑松、红松、华山松、桦木、黄山松、阔叶混交林、冷杉、栎类、楝树、柳杉、柳树、落叶松、马尾松、木荷、木麻黄、楠木、泡桐、其他软阔类、其他松类、其他硬阔类、杉木、水胡黄、水杉、思茅松、铁杉、相思、杨树、油杉、油松、榆树、云南松、云杉、樟木、樟子松、针阔混交林、针叶混交林 Eucalyptus spp., Betula platyphylla, Cupressus spp., Pinus densiflora, Robinia spp., Tilia spp., Betula costata, Liquidambar spp., Pinus densata, foreign pine, Pinus thunbergii, Pinus koraiensis, Pinus armandii, Betula spp., Pinus taiwanensis, mixed broad-leaved forest, Abies spp., Quercus spp., Melia spp., Cryptomeria spp., Salix spp., Larix spp., Pinus massoniana, Schima spp., Casuarina equisetifolia, Phoebe spp., Paulownia spp., other soft-and-broad trees, other pine trees, other hard-and-broad trees, Cunninghamia spp., Fraxinus mandshurica, Juglans mandshurica, Phellodendron, Metasequoia spp., Pinus kesiya var. langbianensis, Tsuga spp., Abrus spp., Populus spp., Keteleeria spp., Pinus tabuliformis, Ulmus spp., Pinus yunnanensis, Picea spp., Cinnamomum spp., Pinus sylvestris var. mongolica, mixed broad-leaf and coniferous forest, mixed coniferous forest

尺度 Scale	独立模型 Independent model				联立方程组模型 Simultaneous equations model
尺度 Scale	R²	SEE/(t?hm^?2)	MPE(%)	TRE(%)	R²	SEE/(t?hm^?2)	MPE(%)	TRE(%)
国家 Nation	0.889 8	12.25	0.14	0.00	0.888 0	12.35	0.15	0.00
区域 Region	0.890 3	12.22	0.14	0.00	0.888 2	12.34	0.15	0.00
行政大区 Administrative region	0.896 9	11.85	0.14	0.00	0.897 0	11.84	0.14	0.00
省份 Province	0.917 5	10.60	0.13	?0.00	0.918 5	10.53	0.12	0.00
国家 Nation	0.889 8	12.25	0.14	0.00	0.884 2	12.55	0.15	0.00
气温区 Temperature region	0.913 9	10.83	0.13	0.00	0.914 5	10.79	0.13	0.00
气候区 Climatic region	0.928 9	9.84	0.12	0.00	0.929 5	9.80	0.12	0.00

Scale-Compatible and Tree Species-Classified Forest Carbon Storage Model of Volume-Derived in China

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建模方式 Modeling mode	参数a Parameter a	参数b Parameter b
独立-尺度 Independent-scale	具体尺度_具体树种_D_林分特征 Specific scale_specific tree species_D_stand characteristics	具体尺度_森林类型层级_D_林分特征 Specific scale_forest type level_D_stand characteristics
独立-树种 Independent-species	具体树种_具体尺度_D_林分特征 Specific tree species_specific scale_D_stand characteristics	具体树种_尺度_D_林分特征 Specific tree species_scale_D_stand characteristics
联立-尺度 Simultaneous-scale	具体尺度_具体树种_L_林分特征 Specific scale_specific tree species_L_stand characteristics	具体尺度_森林类型层级_L_林分特征 Specific scale_forest type level_L_stand characteristics
联立-树种 Simultaneous-species	具体树种_具体尺度_C_L_林分特征 Specific tree species_specific scale_C_L_stand characteristics 具体树种_具体尺度_X_L_林分特征 Specific tree species_specific scale_X_L_stand characteristics	具体树种_尺度_C_L_林分特征 Specific tree species_scale _C _ L_stand characteristics 具体树种_尺度_X_L_林分特征 Specific tree species_scale_X_L_stand characteristics

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